System and method of llm training for network state conversational analytics

Abstract

In some implementations, the method may include receiving, by one or more agent applications, a query from a user. In addition, the method may include providing a dataframe to the one or more agent applications. The method may include appending a predetermined number of initial entries from the dataframe to a suffix of the query. Moreover, the method may include constructing a standardized prompt template, where the query is embedded within the standardized prompt template. Also, the method may include Channeling the prompt template to one or more Large Language Models (LLMs). Further, the method may include Utilizing a GPT API to generate a generated code snippet. In addition, the method may include Executing the generated code snippet to create a resulting output. The method may include Relaying the resulting output back to the user.

What is claimed is: 
     
         1 . A method, comprising:
 receiving, by one or more agent applications a query from a user; providing a dataframe to the one or more agent applications; appending a predetermined number of initial entries from the dataframe to a suffix of the query; constructing a standardized prompt template, wherein the query is embedded within the standardized prompt template;   channeling the standardized prompt template to one or more Large Language Models (LLMs); utilizing a GPT API to generate a generated code snippet;   executing the generated code snippet to create a resulting output;   and relaying the resulting output back to the user.   
     
     
         2 . The method of  claim 1 , further comprising specifying one or more pandas commands to execute in order to formulate an appropriate response to the query. 
     
     
         3 . The method of  claim 1  wherein the relaying is done post-execution. 
     
     
         4 . The method of  claim 2 , wherein the receiving further comprises receiving processed data in CSV format. 
     
     
         5 . The method of  claim 3 , further comprising setting a temperature of the one or more large language models to zero. 
     
     
         6 . A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
 one or more instructions that, when executed by one or more processors of a device, cause the device to:   receive, by one or more agent applications a query from a user; provide a dataframe to the one or more agent applications; append a predetermined number of initial entries from the dataframe to a suffix of the query; construct a standardized prompt template, wherein the query is embedded within the standardized prompt template;   channel the standardized prompt template to one or more Large Language Models (LLMs); utilize a GPT API to generate a generated   code snippet; execute the generated code snippet to   create a resulting output; and relay the resulting output   back to the user.   
     
     
         7 . The non-transitory computer-readable medium of  claim 6 , wherein the one or more instructions further cause the device to:
 specify one or more pandas commands to execute in order to formulate an appropriate response to the query.   
     
     
         8 . The non-transitory computer-readable medium of  claim 7 , wherein the one or more instructions cause the device to receive processed data in CSV format. 
     
     
         9 . The non-transitory computer-readable medium of  claim 6 , wherein the relaying is done post-execution. 
     
     
         10 . The non-transitory computer-readable medium of  claim 9 , wherein the one or more instructions further cause the device to:
 set a temperature of the one or more large language models to zero.   
     
     
         11 . A system comprising:
 one or more processors configured to:   receive, by one or more agent applications a query from a user;   provide a dataframe to the one or more agent applications; append a predetermined number of initial entries from the dataframe to a suffix of the query; construct a standardized prompt template, wherein the query is embedded within the standardized prompt template;   channel the standardized prompt template to one or more Large Language Models (LLMs);   utilize a GPT API to generate a generated code snippet;   execute the generated code snippet to create a resulting output; and   relay the resulting output back to the user.   
     
     
         12 . The system of  claim 11 , further comprising specifying one or more pandas commands to execute in order to formulate an appropriate response to the query. 
     
     
         13 . The system of  claim 12 , wherein the receiving further comprises receiving processed data in CSV format. 
     
     
         14 . The system of  claim 11 , wherein the relaying is done post-execution. 
     
     
         15 . The system of  claim 14 , further comprising setting a temperature of the one or more large language models to zero.